Closes#620#497. Add opt-in Langfuse observability covering all five
model types (chat, embedding, rerank, VLM, ASR) with HTTP-request-scoped
traces and Docker Compose support (both cloud and self-hosted).
Core package internal/tracing/langfuse:
- HTTP client with batched async ingestion (non-blocking in request path)
- Sampling, environment / release tagging, and graceful fallback when
LANGFUSE_* env vars are absent (wrappers become no-ops)
- Gin middleware opens one trace per traced request and finishes it after
the handler chain returns, attaching method / path / user / session
- Trace context is stored under a typed key exported from internal/types
so logger.CloneContext can preserve it across handler / goroutine
boundaries (otherwise each LLM call auto-created an orphan trace,
fragmenting one request into many)
Per-model generation wrappers (opt-in via NewChat/NewEmbedder/...):
- chat: captures prompt, streaming output, token usage + TTFT
- embedding: approximates tokens when the provider omits usage
- rerank: previews query/docs, summarizes results to keep payload small
- vlm: records image count and total bytes, never uploads raw pixels
- asr: records file size and audio duration, never uploads audio bytes
Async title generation (GenerateTitleAsync) now forwards the trace key
into the goroutine so title calls appear under the parent chat trace.
Docker Compose:
- LANGFUSE_* env passthrough on the `app` service for cloud deployments
- Optional `langfuse` profile spins up a self-hosted Langfuse stack that
reuses WeKnora's existing PostgreSQL (separate database via an idempotent
init container that fixes ICU collation drift) and Redis (separate DB
number), adding only ClickHouse, MinIO, web and worker containers
- web/worker entrypoints URL-encode DB_PASSWORD / REDIS_PASSWORD at start
to avoid Prisma P1013 when passwords contain @ / # / etc.
Docs: docs/Langfuse集成.md covers cloud vs self-hosted, per-model usage
strategy, code map, and resource footprint.
This commit introduces the complete wiki feature for WeKnora, enabling AI-powered wiki page generation and management. The implementation includes:
**Backend Changes:**
- Wiki data model: WikiPage type with support for multiple page types (Summary, Entity, Concept, Index, Log)
- Database schema: wiki_pages table with full migration support
- WikiPageService: CRUD operations and page management
- WikiPageRepository: GORM-based persistence layer
- Wiki ingest pipeline: Automated generation of wiki pages from knowledge documents
* Summary page generation using LLM
* Entity and concept extraction in a single LLM call
* Synthesis opportunity detection
* Index page rebuilding
* Log page maintenance
- Wiki boost feature: Enhance chat retrieval with wiki context
- Wiki linting: Maintenance and validation utilities
- Agent wiki tools: Enable agents to query and interact with wiki pages
- Wiki prompts: Comprehensive LLM prompt templates for all wiki generation tasks
- Language support: Reuse existing middleware language infrastructure for LLM prompts
**Frontend Changes:**
- Wiki browser UI: View all wiki pages with filtering and search
- Wiki API client: Knowledge base wiki management endpoints
- Knowledge base editor: Configure wiki settings (language, auto-ingest, synthesis model)
- i18n updates: Support for English, Korean, Russian, and Chinese interfaces
**Configuration:**
- Container DI: Wire up all wiki services
- Router: Register wiki API endpoints
- Task handling: Support async wiki ingest tasks
**Testing:**
- Unit tests for wiki page types
- Service layer tests
- Endpoint tests for wiki operations
- Integration tests with LLM mocking
**Documentation:**
- Language refactoring analysis and guides
- Implementation completion reports
- Quick reference guides for developers
**Key Features:**
✅ LLM-powered wiki page generation from documents
✅ Multi-language support (9+ languages)
✅ Automatic extraction of entities and concepts
✅ Synthesis opportunity detection
✅ Index and log page maintenance
✅ Progressive wiki building across multiple documents
✅ Agent-based wiki interaction
✅ Chat retrieval enhancement with wiki context
✅ Full frontend UI for wiki browsing
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Syncs personal and team Yuque knowledge bases into WeKnora as Markdown,
with full and incremental sync + deletion detection. Mirrors the Feishu
connector pattern; fetches V2Doc.body directly (no async export step).
Config: api_token (required) + base_url (optional, defaults to public cloud).
401/403 wraps ErrInvalidCredentials; 429 honors Retry-After; filters to
status=1 Doc-type at list stage to conserve API quota. Body is trusted as
Markdown for format=markdown|lake — empirically verified against the v2
API: Lake XML lives separately in body_lake, body itself is the Markdown
view. Other formats (e.g. html) are skipped with a placeholder item so
they surface in sync logs instead of silently feeding HTML to the parser.
v2Doc.status accepts both int and string shapes — Yuque's OpenAPI spec
declares string but the live API returns int. sanitizeFileName truncates
at a UTF-8 rune boundary so multi-byte titles don't emit invalid UTF-8
that downstream filename validation rejects.
Also adds logger.SetOutput test hook, used by the token-redaction test.
- Extend RetrieveEngineRegistry with byStoreID map for DB store engines
- Add StoreRegistry interface and EngineFactory function type
- Create engine factory with per-driver SDK client construction
- Load VectorStore records from DB into registry at startup
- Wire Create/Delete to dynamically register/unregister engines
- Add comprehensive unit tests for registry and service
- Added a new logger for LLM calls, enabling detailed logging of model interactions, including request and response data.
- Introduced configuration options for enabling and specifying the log directory for LLM debug logs.
- Implemented cleanup for old log files to manage disk space effectively.
- Wrapped existing chat implementations to log calls when debug logging is enabled, enhancing traceability for model interactions.
Introduce KnowledgePostProcess task to decouple image multimodal
processing from downstream operations like summary, question
generation, and knowledge graph extraction.
This allows unifying the processing logic across documents and
images and ensures all text content, including from images,
is properly extracted before post-processing.
- Removed support for video file uploads across the application, including updates to the knowledge base and document processing services.
- Updated internationalization files to reflect the removal of video-related terminology, changing labels and descriptions to focus solely on audio processing.
- Adjusted file type handling to exclude video formats, ensuring users receive accurate feedback regarding supported file types.
- Enhanced error handling to provide clear messages when users attempt to upload unsupported video files.
This update streamlines the application by focusing on audio processing capabilities and improving user experience through clearer messaging.
Wire VectorStoreService to HTTP with 8 endpoints: types metadata, CRUD
(create/list/get/update/delete), and connection testing (raw + by ID).
Register routes, DI container bindings, and add API documentation.
Allow configuring custom WebSocket and API base URLs for WeCom
via optional credentials fields (ws_endpoint, api_base_url), enabling
integration with privately deployed WeCom instances.
Changes:
- LongConnClient and WebhookAdapter accept custom endpoint URLs
- Endpoint URLs validated at construction (scheme + SSRF check)
- Private deployment hostnames added to file download SSRF allowlist
- Frontend forms added for both WebSocket and Webhook modes
- i18n support for zh-CN, en-US, ru-RU, ko-KR
Uses default public cloud endpoints when not specified.
- Remove infrastructure/crypto package (CryptoService, config, PBKDF2-based encryption)
- Replace with lightweight utils.GetAESKey/EncryptAESGCM/DecryptAESGCM helpers
- Remove cryptoSvc dependency from modelService, tenantService, weKnoraCloudService
- Remove crypto state persistence logic from container initialization
- Add TruncatePromptTokens field to WeKnoraCloud embed request
- Update frontend hint text for WeKnoraCloud credential errors
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Extended the IMChannel interface to include WeChat as a platform, supporting long-polling mode and full output mode.
- Implemented WeChat QR code login functionality, allowing users to authenticate via a QR code.
- Added new API endpoints for generating and polling the status of WeChat QR codes.
- Updated the frontend to handle WeChat-specific UI elements and interactions, including dynamic form adjustments based on the selected platform.
- Enhanced localization files to include WeChat-related translations.
- Introduced a WeChat adapter for message handling and file downloading via the iLink Bot API.
Go 1.24 has an internal compiler race condition on Windows that causes
'package without types' errors during parallel compilation. Setting
GOFLAGS="-p=1" serializes the build and avoids triggering the bug.
- Implement Validate, ListResources, FetchAll, FetchIncremental
- Per-record database sync with individual knowledge items
- Incremental sync with record-level change detection for databases
- Tree-structured resource listing with parent-child relationships
- Markdown renderer for all Notion block types including nested content
- Register NotionConnector in DI container
- Add unit tests for connector and markdown
WeCom AI Bot does not strip @mention from text.content in group chats,
causing all slash commands (/stop, /clear, /help) to fail — the content
arrives as "@BotName /stop" instead of "/stop".
- Add stripAtMentionBasic (stateless) and stripAtMention (with bot name
caching) to handle multi-word bot display names
- Support bot_name in channel credentials for immediate precision
- Apply stripping in both WebSocket and webhook adapters, text and mixed
- Add mention_test.go with 21 test cases
- Updated relevant files to include provider registration, implementation, and metadata.
- Enhanced frontend components to support provider management and configuration.
- Added localization for new provider settings and messages.
- Implemented backend repository methods for CRUD operations on web search providers.
- Deleted WeCom document connector and callback handling code to streamline the datasource management.
- Removed associated localization entries and UI elements related to WeCom integration.
- Updated data source management logic to eliminate references to WeCom, enhancing maintainability and clarity.
- Adjusted error handling and logging to reflect the removal of WeCom-specific functionality.
- Implemented WeCom document connector to sync smart documents and WeDrive files into WeKnora.
- Added detailed documentation for WeCom integration, including setup instructions for callback URL and API access.
- Enhanced frontend localization to support WeCom-specific messages and instructions.
- Updated data source management UI to include WeCom configuration options and callback URL handling.
- Improved error handling and validation for WeCom API interactions.
- Added a new API for managing data sources, including CRUD operations and connection validation.
- Introduced UI components for data source settings, sync logs, and editor dialog, enhancing user experience in managing external data sources.
- Updated localization files to support new data source features in multiple languages.
- Implemented cron expression humanization for better scheduling visibility.
- Enhanced knowledge base editor to include data source management options.
- Updated the assistant descriptions in various prompt templates to specify that they are developed by Tencent.
- Added new intent-specific system prompt templates for greeting, chitchat, follow-up, image analysis, summarization, and web search unavailability scenarios.
- Enhanced citation rules in the system prompts to ensure proper inline citation formatting.
- Refactored the rewrite prompt to classify user intent more accurately and output structured JSON responses.
- Add DingTalk AI Card streaming support with create/deliver/update via OpenAPI
- Extract shared think-block rendering into reusable im.TransformThinkBlocks
- Add stream orphan reaper and edit throttling for DingTalk and Telegram
- Apply think-block transform on all reply paths (DingTalk, Telegram, Feishu)
- Add Card Template ID config to frontend IM channel panel with i18n
Add DingTalk bot adapter supporting both webhook (HmacSHA256 signature
verification) and Stream mode (via official dingtalk-stream-sdk-go),
with sessionWebhook/OpenAPI reply fallback and AccessToken caching.
Add Telegram Bot API adapter supporting both webhook and long-polling
modes with streaming replies via editMessageText, file download via
getFile API, and timing-safe secret token verification.
- Introduced a new IMChannelPanel component for managing WeCom and Feishu channels.
- Added CRUD operations for IM channels, including create, update, delete, and list functionalities.
- Enhanced the backend with new API endpoints for IM channel management.
- Updated documentation to reflect changes in IM integration and channel management.
- Improved localization support for new IM-related UI elements across multiple languages.
- add StreamSender interface and streaming output for WeCom/Feishu
- refactor WeCom adapter into separate webhook and websocket files
- add output_mode config for stream/full toggle per platform
- add stream flush batching, dedup cleanup, and think-block filtering
- improve context template to handle irrelevant retrieved info gracefully
- add IM streaming integration tests
- add IM integration documentation with quick start guide
- support webhook and websocket modes for both platforms
- add im_channel_sessions migration for channel-session mapping
- register IM adapters and callback routes
- update config and docker-compose for IM env vars
- Removed the image storage handling from the `_resolve_images` function, which now only decodes images and returns them as inline bytes.
- Updated the function's docstring to clarify that image persistence is managed by the Go App, and the return value for `image_dir_path` is always empty.
- Adjusted related gRPC and protobuf files to remove the `ConvertToPDF` method and its associated types, streamlining the API.
- Enhanced security measures in the frontend by implementing a placeholder for provider images and ensuring proper hydration of protected file images.
These changes improve the clarity and efficiency of image processing and API interactions within the application.
- Added a new `StorageProviderConfig` struct to the `KnowledgeBase` for improved storage provider management, separating provider selection from storage credentials.
- Updated the `UnmarshalJSON` method to auto-populate `StorageProviderConfig` from the legacy `StorageConfig` for backward compatibility.
- Refactored various service methods to utilize the new storage provider configuration, ensuring a smooth transition from the legacy system.
- Implemented database migrations to add the `storage_provider_config` column and migrate existing data, maintaining compatibility with historical knowledge bases.
This update enhances the clarity and flexibility of storage configurations while ensuring compatibility with existing data structures.
- Added a new `.env.lite.example` file for the Lite version, providing a minimal configuration template.
- Updated `.env.example` to remove deprecated variables and include new Docreader settings.
- Enhanced Docker configurations to support the Lite version, including a new Dockerfile for the Docreader service.
- Introduced a Makefile target for building and running the Lite version, along with packaging capabilities.
- Created GitHub workflows for building and releasing Lite binaries, including Homebrew formula support.
- Implemented a new service file for managing the Lite version as a system service.
This update enables a streamlined, single-binary deployment of WeKnora, reducing external dependencies and simplifying setup.
- Updated the API to include disabled agent IDs for better management of user-specific agent visibility.
- Introduced a new function to set the disabled state for shared agents, allowing users to manage their shared agent preferences.
- Added a new Skills API to retrieve available skills, enhancing the overall agent capabilities.
- Improved the AgentSelector and related components to filter out disabled agents, ensuring a cleaner user experience.
- Updated localization strings to reflect new features and improve clarity in agent and skill management.
This update enhances the user experience by providing better control over agent visibility and introducing new skill management functionalities.
- Updated the AgentSelector component to improve agent selection logic, ensuring accurate representation of selected agents.
- Introduced new AgentShareSettings component for managing agent sharing with organizations, including UI for selecting organizations and displaying shared agents.
- Enhanced API interactions for sharing agents, including new methods for listing and managing agent shares.
- Improved localization support for agent sharing features across multiple languages.
- Updated various components to reflect changes in agent capabilities and sharing status, enhancing user experience and clarity.
This update strengthens the agent management features, providing users with better tools for sharing and selecting agents within organizations.
wizardchen